We present LodeSTAR, an unsupervised, single-shot object detector for microscopy. LodeSTAR exploits the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. We demonstrate that LodeSTAR is comparable to state-of-the-art, supervised deep learning methods, despite training on orders of magnitude less training data, and no annotations. Moreover, we demonstrate that LodeSTAR achieves near theoretically optimal results in terms of sub-pixel positioning of objects of various shapes. Finally, we show that LodeSTAR can exploit additional symmetries to measure additional particle properties, such as the axial position of objects and particle polarizability.
We present LodeSTAR, a label-free, single-shot particle tracker. We design a method for exploiting the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy and that it reliably tracks experimental data of packed cells. Finally, we show that LodeSTAR can exploit additional symmetries to extend the measurable particle properties to the axial position of objects and particle polarizability.
Label-free characterization of biological matter across scales was recorded at SPIE Optics + Photonics held in San Diego, California, United States 2022.
Particles with dimensions smaller than the wavelength of visible light are essential in many fields. As particle size and composition greatly influence particle function, fast and accurate characterisation of these properties is important. Traditional approaches use the Brownian motion of the particles to deduce their size, and therefore requires to observe the particles for many consecutive time-steps. In addition, such techniques can only be applied in environments with known viscosity, hindering characterization in complex environments.
In this work, we demonstrate characterisation of subwavelength particle size and refractive index surpassing that of traditional methods using two orders of magnitude fewer observations of each particle, with no reference to particle motion. This opens up the possibility to characterise and temporally resolve the properties of subwavelength particles in complex environments where the relation between particle dynamics and size is unknown.
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